Identification of selective LdDHFR inhibitors using quantum chemical and molecular modeling approach

Abstract

Among the various known targets for the treatment of Leishmaniasis, dihydrofolate reductase (DHFR) is an essential target which plays an important role in the folate metabolic pathway. In the current study, pharmacoinformatics approaches including quantum chemistry methods, molecular docking and molecular dynamics simulations have been utilized to identify selective Leishmania donovani DHFR (LdDHFR) inhibitors. Initially, for the design of new LdDHFR inhibitors, a virtual combinatorial library was created by considering various head groups (scaffolds), linkers and tail groups. The scaffolds utilized in the library design were selected on the basis of their proton affinity (PA) estimated using quantum chemical methods, required to make a strong H-bond interaction with negatively charged LdDHFR active site. Later on, molecular docking-based virtual screening was performed to screen the designed library. Selectivity of the chosen hits toward the LdDHFR was established through re-docking in the human DHFR enzyme (HsDHFR). Best five hits were subjected to molecular dynamics (MD) simulations to validate their selectivity as well as stability in LdDHFR. Out of the five hits, four were found to be energetically more favorable and promising for selective binding toward LdDHFR in comparison to HsDHFR. Communicated by Ramaswamy H. Sarma